Analysis and Modelling of Truck Parking in Downtown Toronto Matthew Roorda, Adam Wenneman May 13, 2014 [email protected] P 1 Cost, Congestion, and Conflict The “last mile” is the most expensive portion of the supply chain. Commercial vehicles (CVs) in Toronto incurred over $27M in parking citations in 2012. Commercial vehicle (CV) parking is a major source of congestion in urban areas. CV loading activities lead to over 476,000,000 vehicle-hours of delay every year in the US. 2 Cost, Congestion, and Conflict (cont.) Illegally parked CVs can result in safety issues for other road users. In NYC, 14% of curbside deliveries result in a conflict with a cyclist. The problems intensify as cities grow. 3 Research Questions Does the built environment have an impact on illegal parking? Are parking infractions occurring because of inadequate parking supply? Does increased supply reduce illegal parking? Can we simulate the process of parking search and evaluate parking policies? 7 Two Research Approaches Phase I - Parking Ticket Analysis Phase II - Integrated Traffic / Parking Simulation Model Phase I - Parking Ticket Analysis Method Collect information on parking supply, parking demand, and parking citations Aggregate to postal code 4:00 — 6:00 PM Analyze spatial distribution of data to identify patterns Estimate regression models to quantify relationships 9 Study Area for Parking Ticket Analysis 10 Study Area for Parking Ticket Analysis 11 Parking Supply Complete parking inventory of Toronto CBD Multiple categories On-street Surface lot & parking garage Loading bay & loading zone Varies by time of day Policy restrictions Competition 12 Parking Demand Freight trip generation (FTG) model Employment-based Segmented by industry classification Parameters estimated for Greater Golden Horseshoe Commercial Vehicle Model Business establishment classification, location, and employment from InfoCanada 13 Parking Tickets Tickets for all vehicles available from City of Toronto Open Data FOI needed to identify commercial vehicles 630,280 CV tickets in Toronto Over $27M in fines 14 Freight Trips Generated (FTG) CV Accessible Parking Spaces 16 CV Parking Tickets Regression Model for CV Tickets Dependent variable: Parking ticket density 18 Regression Model for CV Tickets Independent variables: Freight Trips Generated (FTG) FTG density (FTG/road meter) Number of loading zone spaces Number of loading bay doors Number of on-street parking spaces Density of on-street parking spaces Number of on-street standing spaces Density of on-street standing spaces Number of surface lot spaces 19 Phase I - Conclusions There is a link between Freight Trip Generation and illegal commercial vehicle parking It is unclear whether parking infractions occurring because of inadequate parking supply. It is unclear whether increased supply reduce illegal parking Aggregation of data may be masking effects Missing the effect of car/truck competition 20 Phase II – Traffic/Parking Simulation Looking at the process of ‘parking search’ 30% of vehicles cruising Parking choice model Microsimulation Compare alternative policies Useful tool for policy makers 21 Additional Data Collection Driver Interviews • 200 drivers interviewed • Short, multiple choice, and a few qualitative questions • Information collected: • Parking location, facility type, arrival time, etc. • Delivery location, type of goods, total weight, etc. • Driver’s difficulties and experiences Parking Choice Model • • Binary logit model Select Spot Truck arrives at parking spot Choice is a function of: Wait for better spot • Availability of a spot • Suitability for truck parking • Distance from parking spot to destination • Facility type (e.g. loading bay vs street parking) Parking Choice Model TABLE 1 Binary Choice Model for Freight Vehicle Parking Location Log Likelihood -84.35 Pseudo R-squared 0.3086 Variable Coefficient Distance to destination -6.23E-03 On street parking facility -1.61 Loading bay parking facility 2.21 Constant 2.12 t-stat -3.87 -4.11 2.09 6.09 Traffic Simulation • Software: • Paramics • car-by-car simulation • Inputs: • Detailed road network • Parking facility type, location, and capacity • Truck and passenger vehicle demand matrices Choice-Simulation Model Integration • Vehicles are tracked when within 250 m of destination Choice-Simulation Model Integration • Tracked vehicles evaluate parking facilities using the binary choice model Choice-Simulation Model Integration • If no spot selected, vehicles cruise around the block and try again Choice-Simulation Model Integration • Once parked, vehicles dwell at the spot for a modelled dwell time and then depart. Simulated Scenarios Scenario 1: Scenario 2 Interior streets reserved for trucks Interior streets reserved for trucks All trucks park on interior streets Trucks may also park on other roads 30 Simulation Results 31 Potential Solutions Space management Parking information Dynamic pricing Parking reservation Off-peak deliveries 32 Thank you Matthew Roorda, Adam Wenneman May 13, 2014 [email protected] P 33
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